}

  /**
   * Coerces an IPv6 address into an IPv4 address.
   *
   * <p>HACK: As long as applications continue to use IPv4 addresses for indexing into tables,
   * accounting, et cetera, it may be necessary to <b>coerce</b> IPv6 addresses into IPv4 addresses.
   * This method does so by hashing 64 bits of the IPv6 address into {@code 224.0.0.0/3} (64 bits
   * into 29 bits):
   *
   * <ul>

// using a delete-marker and MetaSys entries. It's used to track tiered content of a
// deleted/overwritten version. This version is visible _only_to the scanner routine, for subsequent deletion.
// This kind of tracking is necessary since a version's tiered content is deleted asynchronously.

// Backend directory tree structure:
// disk1/
// └── bucket
//     └── object
//         ├── a192c1d5-9bd5-41fd-9a90-ab10e165398d

Thus <code>0(FP)</code> is the first argument to the function,
<code>8(FP)</code> is the second (on a 64-bit machine), and so on.
However, when referring to a function argument this way, it is necessary to place a name
at the beginning, as in <code>first_arg+0(FP)</code> and <code>second_arg+8(FP)</code>.
(The meaning of the offset—offset from the frame pointer—distinct

	// bucket.Methods(http.MethodPut).Path("/{object:.+}").HeadersRegexp("X-Amz-Copy-Source", ".*?(\\/|%2F).*?").HandlerFunc(api.CopyObjectPartHandler).Queries("partNumber", "{partNumber:[0-9]+}", "uploadId", "{uploadId:.*}")
	// Its necessary to set the "X-Amz-Copy-Source" header for the request to be accepted by the handler.
	nilReq.Header.Set("X-Amz-Copy-Source", url.QueryEscape(SlashSeparator+nilBucket+SlashSeparator+nilObject))

`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with...

* Implementers of the cloud provider interface will note the addition of a context to this interface. Trivial code modification will be necessary for a cloud provider to continue to compile. ([#59287](https://github.com/kubernetes/kubernetes/pull/59287), [@cheftako](https://github.com/cheftako))

	maps.Copy(oi2Map, oi2.UserTags)
	if (oi2.UserTagCount > 0 && !reflect.DeepEqual(oi2Map, t.ToMap())) || (oi2.UserTagCount != len(t.ToMap())) {
		return replicateMetadata
	}

	// Compare only necessary headers
	compareKeys := []string{
		"Expires",
		"Cache-Control",
		"Content-Language",
		"Content-Disposition",
		"X-Amz-Object-Lock-Mode",
		"X-Amz-Object-Lock-Retain-Until-Date",

    val framesThatFillWindow =
      roundUp(Settings.DEFAULT_INITIAL_WINDOW_SIZE, peer.maxOutboundDataLength())

    // Write the mocking script. This accepts more data frames than necessary!
    peer.sendFrame().settings(Settings())
    peer.acceptFrame() // SETTINGS ACK
    peer.acceptFrame() // SYN_STREAM on stream 1
    for (i in 0 until framesThatFillWindow) {

  * After your master is upgraded to 1.6, you will need to update your PodSpecs to set the `tolerations` field of the PodSpec and remove the `scheduler.alpha.kubernetes.io/tolerations` annotation on the Pod. (It's not strictly necessary to remove the annotation, as it will have no effect once you upgrade to 1.6.) Between the time the master is upgraded to 1.6 and when you do this, tolerations attached to Pods that are created will have no effect. Pods that are already running will...

- resource metrics endpoint `/metrics/resource/v1alpha1` as well as all metrics under this endpoint have...